Memory retention enhancement for electronic text

ABSTRACT

Aspects of the present disclosure relate to enhancing reading retention of users reading electronic text. A set of user data associated with a user currently reading electronic text on a device is received, the set of user data indicative of a reading retention of the user. The set of user data is analyzed to determine whether a retention enhancement action should be issued. In response to a determination that a retention action should be issued, the retention enhancement action is issued at the device the user is currently reading electronic text on.

BACKGROUND

The present disclosure relates generally to the field of electronictext, and in particular to enhancing memory retention of users readingelectronic text.

A wealth of information is available for reading online. This includes,among other types of content, electronic encyclopedias, news, e-books,digitized print, and social media posts. As electronic text sources(e.g., digital libraries, electronically accessible news, social media,etc.) become more popular, as too does reading informationelectronically.

SUMMARY

Embodiments of the present disclosure include a method, computer programproduct, and system for enhancing reading retention of users readingelectronic text. A set of user data associated with a user currentlyreading electronic text on a device can be received, the set of userdata indicative of a reading retention of the user. The set of user datacan be analyzed to determine whether a retention action should beissued. In response to a determination that a retention action should beissued, the retention action can be issued at the device the user iscurrently reading electronic text on.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present disclosure are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative oftypical embodiments and do not limit the disclosure.

FIG. 1 is a block diagram illustrating an example computing environmentin which illustrative embodiments of the present disclosure can beimplemented.

FIG. 2 is a block diagram illustrating a retention enhancement system,in accordance with embodiments of the present disclosure.

FIG. 3 is a flow-diagram illustrating an example method for enhancingretention of a user reading electronic text, in accordance withembodiments of the present disclosure.

FIG. 4 is a diagram illustrating a cloud computing environment, inaccordance with embodiments of the present disclosure.

FIG. 5 is a block diagram illustrating abstraction model layers, inaccordance with embodiments of the present disclosure.

FIG. 6 is a high-level block diagram illustrating an example computersystem that may be used in implementing one or more of the methods,tools, and modules, and any related functions, described herein, inaccordance with embodiments of the present disclosure.

While the embodiments described herein are amenable to variousmodifications and alternative forms, specifics thereof have been shownby way of example in the drawings and will be described in detail. Itshould be understood, however, that the particular embodiments describedare not to be taken in a limiting sense. On the contrary, the intentionis to cover all modifications, equivalents, and alternatives fallingwithin the spirit and scope of the disclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure relate generally to the field ofelectronic text, and in particular to enhancing retention for usersreading electronic text. While the present disclosure is not necessarilylimited to such applications, various aspects of the disclosure can beappreciated through a discussion of various examples using this context.

A wealth of information is available for reading online. This includes,among other types of content, electronic encyclopedias, news, e-books,digitized print, and social media posts. As electronic text sources(e.g., digital libraries, electronically accessible news, social media,etc.) become more popular, as too does reading informationelectronically.

When reading electronic text, users often lose focus and concentrationover time. Consequently, users may not retain all of the informationthey read. This can be amplified for users with conditions alreadyaffecting their reading abilities (e.g., attention-deficit/hyperactivitydisorder, dyslexia, etc.). Currently, there is no known solution foridentifying a degraded retention level of a user and issuing a retentionaction to enhance the user's reading retention.

Aspects of the present disclosure are directed to enhancing readingretention for users reading electronic text. A set of user data can bereceived for a user currently reading electronic text on a device. Theset of user data can include, among other sources, eye-tracking data,electronic device interaction data, biometric data, and facialrecognition data. The set of user data can be analyzed to determinewhether a degraded retention level is observed. In response todetermining that a degraded retention level is observed, a retentionenhancement action can be issued. The retention enhancement action caninclude, among other actions, altering text characteristics (e.g., font,color, size, boldness, underlining, etc.), altering text zoom level, andletter scrambling.

Turning now to the Figures, FIG. 1 is a block diagram illustrating anexample computing environment 100 in which illustrative embodiments ofthe present disclosure can be implemented. Computing environment 100includes a plurality of devices 105-1, 105-2 . . . 105-N (collectivelydevices 105), at least one server 135, and a network 150.

Consistent with various embodiments, the server 135 and the devices 105are computer systems. The devices 105 and the server 135 include one ormore processors 115-1, 115-2 . . . 115-N (collectively processors 115)and 145 and one or more memories 120-1, 120-2 . . . 120-N (collectivelymemories 120) and 155, respectively. The devices 105 and the server 135can be configured to communicate with each other through internal orexternal network interfaces 110-1, 110-2 . . . 110-N (collectivelynetwork interfaces 110) and 140. The network interfaces 110 and 140 are,in some embodiments, modems or network interface cards. The devices 105and/or the server 135 can be equipped with a display or monitor.Additionally, the devices 105 and/or the server 135 can include optionalinput devices (e.g., a keyboard, mouse, scanner, video camera,eye-tracking device, wearable device, or other input device), and/or anycommercially available or custom software (e.g., browser software,communications software, server software, natural language processingsoftware, search engine and/or web crawling software, image processingsoftware, eye-tracking software, facial expression recognition software,biometric reading software, etc.). The devices 105 and/or the server 135can be servers, desktops, laptops, or hand-held devices.

The devices 105 and the server 135 can be distant from each other andcommunicate over a network 150. In some embodiments, the server 135 canbe a central hub from which devices 105 can establish a communicationconnection, such as in a client-server networking model. Alternatively,the server 135 and devices 105 can be configured in any other suitablenetworking relationship (e.g., in a peer-to-peer (P2P) configuration orusing any other network topology).

In some embodiments, the network 150 can be implemented using any numberof any suitable communications media. For example, the network 150 canbe a wide area network (WAN), a local area network (LAN), an internet,or an intranet. In certain embodiments, the devices 105 and the server135 can be local to each other and communicate via any appropriate localcommunication medium. For example, the devices 105 and the server 135can communicate using a local area network (LAN), one or more hardwireconnections, a wireless link or router, or an intranet. In someembodiments, the devices 105 and the server 135 can be communicativelycoupled using a combination of one or more networks and/or one or morelocal connections. For example, the first device 105-1 can be hardwiredto the server 135 (e.g., connected with an Ethernet cable) while thesecond device 105-2 can communicate with the server 135 using thenetwork 150 (e.g., over the Internet).

In some embodiments, the network 150 is implemented within a cloudcomputing environment, or using one or more cloud computing services.Consistent with various embodiments, a cloud computing environment caninclude a network-based, distributed data processing system thatprovides one or more cloud computing services. Further, a cloudcomputing environment can include many computers (e.g., hundreds orthousands of computers or more) disposed within one or more data centersand configured to share resources over the network 150.

The server 135 includes a retention enhancement application 160. Theretention enhancement application 160 can be configured to enhance theretention (e.g., focus, ability to ingest information, alertness, etc.)of a user reading electronic text. To do so, the retention enhancementapplication 160 can be configured to collect user data (e.g., dataregarding the user's interaction with their electronic display,eye-tracking data, facial expression data, biometric data, etc.),analyze the user data to determine whether a retention action should beissued (e.g., by comparing a retention level of the user to a retentionthreshold), and execute, in response to a determination that a retentionaction should be issued, the retention action. In embodiments, theretention action can include modifying the electronic text (e.g., thesize, font, color, boldness, word scrambling, etc.) displayed on theuser's device (e.g., device 105-1).

Though this disclosure pertains to the collection of personal data, itis noted that in embodiments, users opt-in to the system. In doing so,they are informed of what data is collected and how it will be used,that any collected personal data may be encrypted while being used, thatthe users can opt-out at any time, and that if they opt-out, anypersonal data of the user is deleted.

In embodiments, data associated with the retention enhancementapplication 160 can be transmitted to the devices 105 on a push or pullbasis. Further, in embodiments, the retention enhancement application160 can be installed directly on the devices 105, or alternatively,provisioned to the devices 105 over the network 150 such thatinstallation is not necessary.

It is noted that FIG. 1 is intended to depict the representative majorcomponents of an example computing environment 100. In some embodiments,however, individual components can have greater or lesser complexitythan as represented in FIG. 1, components other than or in addition tothose shown in FIG. 1 can be present, and the number, type, andconfiguration of such components can vary.

While FIG. 1 illustrates a computing environment 100 with a singleserver 135, suitable computing environments for implementing embodimentsof this disclosure can include any number of servers. The variousmodels, modules, systems, and components illustrated in FIG. 1 canexist, if at all, across a plurality of servers and devices. Forexample, some embodiments can include two servers. The two servers canbe communicatively coupled using any suitable communications connection(e.g., using a WAN, a LAN, a wired connection, an intranet, or theInternet).

FIG. 2 is a block diagram illustrating an example computing environment200 in which illustrative embodiments of the present disclosure can beimplemented. The computing environment 200 includes a device 205 and aretention enhancement system 201. The retention enhancement system(e.g., which may be the same as, or substantially similar to, retentionenhancement application 160 of FIG. 1) includes a data receiving module210, a retention analyzer 215, a user profile data store 220, and aretention enhancer 225. In embodiments, the data receiving module 210,retention analyzer 215, user profile data store 220, and retentionenhancer 225 can include processor executable instructions that can beexecuted by a dedicated or shared processor using received inputs (e.g.,from device 205).

Consistent with various embodiments, the data receiving module 210 canbe configured to receive data from the device 205 (e.g., devices 105 ofFIG. 1). Data received by the data receiving module 210 can include, butis not limited to, images, electronic documents, device interaction data(e.g., mouse movements, scrolling, highlighting, etc.), display data,biometric data, website data, audio data, and/or video data. In someembodiments, the data receiving module 210 can be configured toreformat, tag, or otherwise process the data.

The data receiving module 210 then dispatches the data to the retentionanalyzer 215. The retention analyzer 215 can be configured to determinewhether a retention action should be issued by the retention enhancer225. This can be completed in a variety of manners. For example, theretention analyzer 215 can be configured to determine whether aretention action should be issued by the retention enhancer 225 based oneye-tracking data (e.g., indicative of reading activity and/or speed),mood detection (e.g., facial expression approximation), electronicdevice interactions (e.g., mouse movements, scrolling speed, viewportchanges, etc.), and biometric data.

In some embodiments, the retention analyzer 215 can determine whether aretention action should be issued based on a reading speed of a user.For example, if a user's current reading speed falls a predeterminedamount (e.g., percentage, value, etc.) below their average readingspeed, a determination can be made that a retention action should beissued. Following the above example, assume a retention threshold is setsuch that if a user falls below 50% of their average reading speed, theretention analyzer 215 determines that a retention action should beissued. In this example, if a user's average reading speed is 200words/min and their current reading speed is 70 words/min, adetermination can be made to issue a retention action (e.g., because theuser's current reading speed is less than 50% of their average speed).This can be completed using any suitable threshold. For example, in someembodiments, the retention analyzer 215 can be configured to determinethat a retention action should be issued based on an observed readingspeed falling below a fixed value (e.g., 50 words/min).

Reading speed can be determined in any suitable manner. In someembodiments, reading speed can be determined with the aid ofeye-tracking technology (e.g., collected from an additional device suchas a head mounted display (HMD) or camera over a network). In theseembodiments, the number of words read over time, as measured by theeye-tracking system, can indicate a user's reading speed. In someembodiments, reading speed can be determined based on word throughputthrough a given viewport (e.g., a display), application (e.g., a worddocument, pdf, e-book application, etc.), or website. That is, thereading speed can be determined based on the number of words traversed(e.g., displaced on a user's screen) over a given time interval. As anexample, if a user traverses 500 words (e.g., 500 words are displacedacross the screen) in 2 minutes, a determination can be made that theuser's reading speed is 250 words/min. As another example, if a userreads 2 pages in 3 minutes, and each page has 600 words, then adetermination can be made that the user's reading speed is 400words/min. Reading speed measurements can be stored in the user profiledata store 220 for each user. The totality of reading speed measurementsfor each user can be used to calculate an average reading speed for eachuser.

In some embodiments, the retention analyzer 215 can determine a user'sreading retention based on a mood of the user. For example, theretention analyzer 215 may be configured to perform facial expressioninterpretation (e.g., using supervised or unsupervised machine learning)to determine a mood of the user. This can aid in determining whether theuser is tired, drowsy, fatigued, sad, frustrated, or any other moodindicative of a degraded retention state. In these embodiments, if theretention analyzer 215 outputs a mood indicative of a degraded retentionlevel, then a determination can be made to issue a memory retentionaction by the retention enhancer 225.

In embodiments, the retention analyzer 215 can analyze biometric data(e.g., heart rate, blood glucose level, caffeine intake, respiratoryrate, etc.) to determine if retention actions should be issued by theretention enhancer 225. For example, the retention analyzer 215 canobtain a heart rate of a user via a wearable device (e.g., a smartwatch). The heart rate of the user can then be compared to the user'saverage heart rate to aid in determining whether a retention actionshould be issued. As an example, if a user's current heart rate drops apredetermined level (e.g., 10 bpm, 20%, etc.) below their average heartrate, the retention analyzer 215 can be configured to determine that aretention action should be issued by the retention enhancer 225. Inembodiments, the user's biometric data can be securely stored in theuser profile data store 220. The history of biometric data stored in theuser profile data store 220 can be used to better predict retentiondegradation states in the future. Ultimately, the user profile datastore 220 can be used to select optimal retention actions per user(e.g., based on each user's historical data).

In some embodiments, the retention analyzer 215 can be configured todetermine whether a retention action should be issued by analyzing auser's interactions with an electronic device they are reading on. Forexample, the retention analyzer 215 can be configured to analyze theuser's input activity (e.g., highlighting of text portions with themouse, moving the mouse along portions of the text while reading,scrolling along text with keyboard arrows, scrolling along text with atouch pad, etc.), reading speed (e.g., based on a throughput of wordsthrough a viewport, application, website, etc. of the device), and/ornavigation data (e.g., scrolling speed, which paragraphs and/or lines oftext are visible over time, the number of paragraphs and/or lines oftext visited over time (compared to an average value for each user), the“jump distance” to and from a particular paragraph, etc.) to determine aretention level of the user.

For example, if a user is highlighting and/or following lines of textwith an input device (e.g. a keyboard, touch screen, mouse, etc.),unless a copy/paste action is observed, a determination can be made thatthe user is highlighting and/or following text as a reading aid. Thiscan be used to determine whether a retention action should be issued.For example, based on the speed at which the highlighting or theline-following occurs, a retention action may or may not be issued.Similarly, if a user remains on the same viewport, page, set ofparagraphs, and/or set of sentences of a body of electronic text beyonda predetermined time, a determination can be made to issue a retentionaction by the retention analyzer 215.

In embodiments, multiple inputs (e.g., biometric data, deviceinteractions, mood detection, eye-tracking data, etc.) can becollectively considered by the retention analyzer 215. Because inputactivity (e.g., mouse movements), reading speed, navigation data, mooddata, biometric readings (e.g., heart rate, blood glucose level,caffeine intake, etc.), and/or eye-tracking data may not all beavailable (e.g., if a user is not moving their mouse while reading, ifthere is no biometric data available, if there is no eye-tracking dataavailable, etc.), a generic formula can be used to calculate theretention level. For example, the retention level can be calculatedaccording to Equation (1) depicted below:

$\begin{matrix}{{RDC} = {\frac{1}{\sum\limits_{i = 1}^{n}W_{i}}*{\sum\limits_{\;^{i = 1}}^{n}{C_{i}*W_{i}}}}} & {{Equation}\mspace{14mu}(1)}\end{matrix}$

In Equation (1), RDC (Retention Degradation Confidence) is theconfidence value for a low retention level, C_(i) is a measuredcriterion which contributes to the memory retention level (e.g., mouseactivity, reading speed, navigation data, biometric readings, moodlevel, etc.), and W_(i) is a weight applied to each criterion whichaffects the impact each criterion has on the calculated retentiondegradation confidence. Using Equation (1), the RDC output can be in therange of 0-1, with 1 indicating a high confidence of a low retentionlevel and 0 indicating a low confidence of a low retention level.Following Equation (1), if the following inputs are obtained: C₁ readingspeed 0.7, W₁ 0.6, C₂ mouse movements 1, W₂ 0.2, and C₃ heart rate 0.5,W₃ 0.2, then RDC would be calculated as:

${RDC} = {{\frac{1}{0.6 + 0.2 + 0.2}*\left( {{0.7*0.6} + {1*0.2} + {0.5*0.2}} \right)} = 0.72}$

The retention analyzer 215 can then be configured to compare theretention degradation confidence to a retention degradation confidencethreshold to determine whether a retention action should be issued. Inthis example, if the retention degradation confidence threshold is 0.80,then a determination can be made that the retention degradationconfidence satisfies (e.g., is below) the retention degradationconfidence threshold (as 0.72<0.80), and thus triggers no action (e.g.,or deactivate a retention action if it was active). On the contrary, anyconfidence level calculated as 0.80 or higher would trigger a retentionenhancement action by retention enhancer 225. This is because higherretention levels output by Equation (1) indicate a higher confidence ofa retention degradation (e.g., a low retention level).

Reference will now be made to examples of measuring C_(i) values ofvarious criteria which can impact the retention level of the user.

A reading speed criterion C_(i) value can, in some embodiments, becalculated based on a comparison between a user's current reading speedand their average reading speed. For example, assume that User A has acurrent reading speed of 100 words/min and an average reading speed of180 words per minute (e.g., he is reading at 55.5% of his averagespeed). In this example, if a reading threshold is 50% (e.g., User A isrequired to read at least at 50% of his average reading speed before adegraded retention level is determined), then the C_(i) value can beoutput as 0, as User A exceeds the reading threshold (e.g., 0 indicatesa low confidence of a low retention level). The weight of the readingspeed criterion W_(i) can be selected in any suitable manner. Inembodiments, the weight of the reading speed criterion is relativelyhigh compared to other criteria, as it is a strong indication in whethera user is actively reading/retaining information.

In some embodiments, the C_(i) value for reading speed may not bebinary, but proportional to the distance between the user's currentreading speed and average reading speed. For example, assume that if auser attains their average reading speed, C_(i)=0. Further assume, thatif a user attains at least twice their average reading speed, C_(i)=0(e.g., indicative of a low confidence of a degraded retention). In thisexample, if a user reads at half of their average reading speed, C_(i)can be calculated as 0.75 (e.g., indicative of a high confidence of adegraded retention). Similarly, if the user reads at 0 words/min, theC_(i) value can be calculated as 1 (e.g., indicative of a max confidenceof a degraded retention for the reading criterion). In theseembodiments, as lower reading speeds are observed, the confidence of adegraded retention level increases.

C_(i) values for input activity can be determined in various manners. Insome embodiments, if a user is scrolling and/or following along withtext with an input device (e.g., including mice, keyboards, or touchscreens), a determination can be made input activity is to be includedas an extra criterion C_(i). This is because if a user is followingalong with text, this is an indication that the user is using the inputdevice as a reading aid. However, if no input activity data isavailable, other criteria (e.g., reading speed, mood, etc.) can beconsidered without considering the input activity criterion. Inembodiments, the weight value W_(i) for input activity is relativelylow, as input activity only provides a rough indication of retentionlevel.

In embodiments, a C_(i) value for navigation data can be calculatedbased on how often a user navigates to a particular location (e.g.,page, paragraph, and/or set of sentences) within an electronic document.In these embodiments, navigations to particular locations may only beconsidered if the user stops on the location for a predetermined amountof time (e.g., 10 seconds). If a user navigates to the same locationbeyond a predetermined threshold number of instances, a determinationcan be made that the retention level is degraded, and a high C_(i) valuecan be output. For example, assume a threshold number of instances toreturn to a particular location (e.g., if a user rereads the sameparagraph or page “x” number of times, a degraded retention isdetermined) is four. Following this example, if a user returns to “page10” of an electronic document five times, a determination can be madethat C_(i) is 1. This increases the confidence of a retention leveldegradation.

In embodiments, C_(i) values for mood detection can be calculated basedon whether a mood output indicates a user is in a state indicative of adegraded retention level. For example, if a mood estimation (e.g., basedon an image analysis using a supervised machine learning model)indicates that a user is tired, then the C_(i) value can be output as 1,increasing the confidence of a low retention level. Conversely, if themood estimate indicates that a user is excited, happy, interested,intrigued, or any other mood indicative of a high retention level, thena low C_(i) value can be output, decreasing the confidence of a lowretention level.

In embodiments, C_(i) values for biometric readings can be calculatedbased on a comparison between a user's average biometric readings andtheir current biometric readings. For example, assume a user's averagerespiratory rate is 15 breaths/min. Further, assume that if the user'srespiratory rate falls 33% (e.g., indicative of a drowsy state), a C_(i)value of 1 will be returned. In this example, if a wearable determinesthat a user is currently breathing at a rate of 10 breaths/min, a C_(i)value of 1 can be returned. If the user is currently breathing above 10breaths/min, a C_(i) value of 0 can be returned. This exampleillustrates a binary output, however, in embodiments, the granularity ofthe C_(i) value can vary. For example, the C_(i) value can be a valueproportional to the distance between a user's average biometric readingand current biometric reading.

After the retention level RDC is calculated based on the available inputdata, the RDC is compared to a retention level threshold. If theretention level satisfies (e.g., falls within, does not exceed, etc.)the retention level threshold, then a determination can be made that aretention action is not required. Conversely, if the retention levelexceeds the retention level threshold (e.g., RDC=0.75 while theretention level threshold=0.70), then a determination can be made that aretention action is to be issued by the retention enhancer 225.

After the retention analyzer 215 determines whether or not a retentionaction is to be issued, the retention analyzer 215 dispatches theresulting command to the retention enhancer 225. The retention enhancer225 can then be configured to issue a retention action at the device 205(e.g., the device the user is reading on). Retention actions caninclude, but are not limited to, font adjustments (e.g., font style,bolding, italics, underlining, color, size, etc.), zoom leveladjustments (e.g., increasing the size of words displayed on theviewport), screen brightness adjustments, and/or other actions.

For example, in some embodiments, the retention action can includeclosing one or more other applications (e.g., a video streamingapplication, a music application, etc.) in the background which may becausing a distraction. In some embodiments, the retention action caninclude altering the font of the electronic text to Sans Forgetica, afont developed by researchers at RMIT UNIVERSITY. This font, engineeredusing the principles of cognitive psychology, is designed to enhancereading retention (e.g., based on the angle of the front, letterstructure, etc.). In some embodiments, the retention action can includescrambling letters within words of the electronic text. For example, allletters except the first and last letter of each word may be scrambledsuch that user is required to exert more effort to read the text, whichmay increase reading retention.

In embodiments, after the retention action is issued by the retentionenhancer 225, user data can continue to be collected and analyzedthereafter. This is completed to determine whether the user's retentionlevel has increased such that reversing the retention action isjustified. For example, assume that a retention action (e.g., a fontchange to Sans Forgetica) is issued at a user's electronic device due tothe user's reading speeding falling a predetermined amount below theiraverage reading speed. In this example, if the user's reading speedincreases above the predetermined amount below their average readingspeed, the retention action can be reverted at the user's electronicdevice.

It is noted that FIG. 2 is intended to depict the representative majorcomponents of an example computing environment 200. In some embodiments,however, individual components can have greater or lesser complexitythan as represented in FIG. 2, components other than or in addition tothose shown in FIG. 2 can be present, and the number, type, andconfiguration of such components can vary. For example, though a singledevice 205 is depicted in FIG. 2, more or fewer devices can be present.In some embodiments, a single device can collect user data, analyze theuser data to determine whether a retention actions should be issued, andexecute a retention action. In some embodiments, additional devices(e.g., eye-tracking sensors, biometric reading sensors, etc.) cancollect user data and transmit the user data to the retentionenhancement system 201. In some embodiments, multiple devices or sharedresources (e.g., a cloud computing environment) can collectivelycomplete one or more of the functional aspects of the computing (e.g.,to increase the precision of the analysis).

FIG. 3 is a flow-diagram illustrating an example method 300 forenhancing the retention of a user reading electronic text, in accordancewith embodiments of the present disclosure.

Method 300 initiates at operation 305, where user data is obtained. Userdata can include various sources of data which can be used to determinea retention level of a user. For example, user data can includeelectronic device interaction data, biometric data, eye-tracking data,and facial recognition data. In embodiments, user data can be obtainedover a network (e.g., from one or more additional devices or sensors).The user data can be obtained on a pull or push basis.

The user data is then analyzed. This is illustrated at operation 310. Inembodiments, the analysis can be completed using the same, orsubstantially similar, techniques described with respect to theretention analyzer 215 of FIG. 2. For example, reading speed, inputinteractions, navigation data, biometric data, and/or facial expressiondata can be compared to an average value or state for the user. In someembodiments, the user data can be compared to a fixed threshold. In someembodiments, multiple inputs can be collectively considered. In theseexamples, a retention level can be calculated using a generic/normalizedformula (e.g., see Equation (1)).

A determination is then made whether a retention action should beissued. This is illustrated at operation 315. The determination whethera retention action should be issued can be completed using the same, orsubstantially similar, techniques described with respect to theretention analyzer 215 of FIG. 2. For example, the determination can bemade based on comparison between current and historical data of aparticular user (e.g., average user data), as well as the data of otherusers. In some embodiments, the determination can be made based on athreshold comparison. For example, if a retention level of a usersatisfies a retention level threshold, a determination can be made thata retention enhancement action should be issued.

If a determination is made that a retention action should not be issued,then method 300 proceeds to operation 325, where a determination is madewhether a retention action should be reverted (e.g., if anotherretention action is active).

If a determination is made that a retention action should be issued,then the retention action is issued. This is illustrated at operation320. Issuing the retention action at operation 320 can be completedusing the same, or substantially similar, techniques as described withrespect to the retention enhancer 225 of FIG. 2. For example, retentionactions can include, but are not limited to, electronic textcharacteristic adjustment, screen brightness adjustments, wordscrambling, and zoom level adjustment.

After the retention action is issued, a determination is made whetherthe retention action should be reverted. This is illustrated atoperation 325. Determining whether the retention action should bereverted can be based on continually collected user data. If the userdata indicates that the reading retention of the user has increased toan amount (e.g., above a threshold) such that reverting the retentionaction is justified, the retention action can be reverted. For example,assume a screen brightness adjustment is issued due to a user'sbiometric reading falling below a biometric reading threshold. In thisexample, if the biometric reading later satisfies the biometric readingthreshold, the screen brightness adjustment can be reverted to theprevious screen brightness.

If a determination is made that the retention action should not bereverted, then method 300 terminates. In embodiments, after adetermination is made that the retention action should be not bereverted, user data can continue to be collected (e.g., to determinewhether to issue and/or revert one or more retention actions based onthe user's reading retention).

If a determination is made that the retention action should be reverted,then the retention action is reverted. This is illustrated at operation330. For example, if a retention action includes altering electronictext font from Times New Roman to Sans Forgetica, then the font can bereverted back to Times New Roman. As another example, if the retentionaction includes letter scrambling within words of the electronic text,then the letters of the words can be de-scrambled. Similarly, if theretention action includes closing one or more background applicationswhich may be causing a distraction, then the one or more backgroundapplications which were closed can be relaunched.

In some embodiments, a history of previous retention actions can bereferenced to determine which retention actions should be issued forparticular users in the future. For example, if a text adjustment (e.g.,changing a font to Sans Forgetica) increases a user's retention levelwithin a first time period (e.g., 10 seconds) and a screen brightnessadjustment increases a user's retention level within a second timeperiod (e.g., 1 minute), then a determination can be made that the textadjustment is an effective retention action for the user. In thisexample, text adjustment may be selected as the retention action in thefuture if a degraded retention level is observed.

The aforementioned operations can be completed in any order and are notlimited to those described. Additionally, some, all, or none of theaforementioned operations can be completed, while still remaining withinthe spirit and scope of the present disclosure. For example, in someembodiments, operations 325 and 330 may not be completed, as theretention action may not be reverted.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model can includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but can be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It can be managed by the organization or a third party andcan exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It can be managed by the organizations or a third partyand can exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service-oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 4, illustrative cloud computing environment 410 isdepicted. As shown, cloud computing environment 410 includes one or morecloud computing nodes 400 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 400A, desktop computer 400B (e.g., devices 105,server 135, device 205, retention enhancement system 201) laptopcomputer 400C (e.g., devices 105, server 135, device 205, retentionenhancement system 201), and/or automobile computer system 400N maycommunicate. Nodes 400 may communicate with one another. They may begrouped (not shown) physically or virtually, in one or more networks,such as Private, Community, Public, or Hybrid clouds as describedhereinabove, or a combination thereof. This allows cloud computingenvironment 410 to offer infrastructure, platforms and/or software asservices for which a cloud consumer does not need to maintain resourceson a local computing device. It is understood that the types ofcomputing devices 400A-N shown in FIG. 4 are intended to be illustrativeonly and that computing nodes 400 and cloud computing environment 410can communicate with any type of computerized device over any type ofnetwork and/or network addressable connection (e.g., using a webbrowser).

Referring now to FIG. 5, a set of functional abstraction layers providedby cloud computing environment 410 (FIG. 4) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only and embodiments of thedisclosure are not limited thereto. As depicted below, the followinglayers and corresponding functions are provided.

Hardware and software layer 500 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 502;RISC (Reduced Instruction Set Computer) architecture-based servers 504;servers 506; blade servers 508; storage devices 510; and networks andnetworking components 512. In some embodiments, software componentsinclude network application server software 514 and database software516.

Virtualization layer 520 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers522; virtual storage 524; virtual networks 526, including virtualprivate networks; virtual applications and operating systems 528; andvirtual clients 530.

In one example, management layer 540 may provide the functions describedbelow. Resource provisioning 542 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 544provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.For example, security may include protecting (e.g., via a cryptographichash function) user data which is used to determine whether a retentionenhancement action should be issued. User portal 546 provides access tothe cloud computing environment for consumers and system administrators.Service level management 548 provides cloud computing resourceallocation and management such that required service levels are met.Service Level Agreement (SLA) planning and fulfillment 550 providepre-arrangement for, and procurement of, cloud computing resources forwhich a future requirement is anticipated in accordance with an SLA.

Workloads layer 560 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 562; software development and lifecycle management 564;virtual classroom education delivery 566; data analytics processing 568;transaction processing 570; and retention enhancement 572.

Referring now to FIG. 6, shown is a high-level block diagram of anexample computer system 601 (e.g., devices 105, server 135, device 205,retention enhancement system 201) that may be used in implementing oneor more of the methods, tools, and modules, and any related functions,described herein (e.g., using one or more processor circuits or computerprocessors of the computer), in accordance with embodiments of thepresent disclosure. In some embodiments, the major components of thecomputer system 601 may comprise one or more CPUs 602, a memorysubsystem 604, a terminal interface 612, a storage interface 614, an I/O(Input/Output) device interface 616, and a network interface 618, all ofwhich may be communicatively coupled, directly or indirectly, forinter-component communication via a memory bus 603, an I/O bus 608, andan I/O bus interface unit 610.

The computer system 601 may contain one or more general-purposeprogrammable central processing units (CPUs) 602A, 602B, 602C, and 602D,herein generically referred to as the CPU 602. In some embodiments, thecomputer system 601 may contain multiple processors typical of arelatively large system; however, in other embodiments the computersystem 601 may alternatively be a single CPU system. Each CPU 602 mayexecute instructions stored in the memory subsystem 604 and may includeone or more levels of on-board cache.

System memory 604 may include computer system readable media in the formof volatile memory, such as random access memory (RAM) 622 or cachememory 624. Computer system 601 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 626 can be provided forreading from and writing to a non-removable, non-volatile magneticmedia, such as a “hard-drive.” Although not shown, a magnetic disk drivefor reading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), or an optical disk drive for reading from orwriting to a removable, non-volatile optical disc such as a CD-ROM,DVD-ROM or other optical media can be provided. In addition, memory 604can include flash memory, e.g., a flash memory stick drive or a flashdrive. Memory devices can be connected to memory bus 603 by one or moredata media interfaces. The memory 604 may include at least one programproduct having a set (e.g., at least one) of program modules that areconfigured to carry out the functions of various embodiments.

One or more programs/utilities 628, each having at least one set ofprogram modules 630 may be stored in memory 604. The programs/utilities628 may include a hypervisor (also referred to as a virtual machinemonitor), one or more operating systems, one or more applicationprograms, other program modules, and program data. Each of the operatingsystems, one or more application programs, other program modules, andprogram data or some combination thereof, may include an implementationof a networking environment. Programs 628 and/or program modules 630generally perform the functions or methodologies of various embodiments.

In some embodiments, the program modules 630 of the computer system 601may include a retention enhancement module. The retention enhancementmodule can be configured to obtain user data for a user currentlyreading electronic text on a device, analyze the user data to determinewhether a retention action should be issued, and execute a retentionaction in response to a determination that the retention action shouldbe issued.

Although the memory bus 603 is shown in FIG. 6 as a single bus structureproviding a direct communication path among the CPUs 602, the memorysubsystem 604, and the I/O bus interface 610, the memory bus 603 may, insome embodiments, include multiple different buses or communicationpaths, which may be arranged in any of various forms, such aspoint-to-point links in hierarchical, star or web configurations,multiple hierarchical buses, parallel and redundant paths, or any otherappropriate type of configuration. Furthermore, while the I/O businterface 610 and the I/O bus 608 are shown as single respective units,the computer system 601 may, in some embodiments, contain multiple I/Obus interface units 610, multiple I/O buses 608, or both. Further, whilemultiple I/O interface units are shown, which separate the I/O bus 608from various communications paths running to the various I/O devices, inother embodiments some or all of the I/O devices may be connecteddirectly to one or more system I/O buses.

In some embodiments, the computer system 601 may be a multi-usermainframe computer system, a single-user system, or a server computer orsimilar device that has little or no direct user interface, but receivesrequests from other computer systems (clients). Further, in someembodiments, the computer system 601 may be implemented as a desktopcomputer, portable computer, laptop or notebook computer, tabletcomputer, pocket computer, telephone, smart phone, network switches orrouters, or any other appropriate type of electronic device.

It is noted that FIG. 6 is intended to depict the representative majorcomponents of an exemplary computer system 601. In some embodiments,however, individual components may have greater or lesser complexitythan as represented in FIG. 6, components other than or in addition tothose shown in FIG. 6 may be present, and the number, type, andconfiguration of such components may vary.

As discussed in more detail herein, it is contemplated that some or allof the operations of some of the embodiments of methods described hereinmay be performed in alternative orders or may not be performed at all;furthermore, multiple operations may occur at the same time or as aninternal part of a larger process.

The present disclosure may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer-readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer-readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer-readable program instructions described herein can bedownloaded to respective computing/processing devices from acomputer-readable storage medium or to an external computer or externalstorage device via a network, for example, the Internet, a local areanetwork, a wide area network and/or a wireless network. The network maycomprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computers,and/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer-readable programinstructions from the network and forwards the computer-readable programinstructions for storage in a computer-readable storage medium withinthe respective computing/processing device.

Computer-readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine-dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including anobject-oriented programming language such as Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. Thecomputer-readable program instructions may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider). In some embodiments, electronic circuitry including, forexample, programmable logic circuitry, field-programmable gate arrays(FPGA), or programmable logic arrays (PLA) may execute the computerreadable program instructions by utilizing state information of thecomputer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer-readable program instructions.

These computer-readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer-readable program instructionsmay also be stored in a computer-readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that thecomputer-readable storage medium having instructions stored thereincomprises an article of manufacture including instructions whichimplement aspects of the function/act specified in the flowchart and/orblock diagram block or blocks.

The computer-readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce acomputer-implemented process, such that the instructions which executeon the computer, other programmable apparatus, or other device implementthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the variousembodiments. As used herein, the singular forms “a,” “an,” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“includes” and/or “including,” when used in this specification, specifythe presence of the stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. In the previous detaileddescription of example embodiments of the various embodiments, referencewas made to the accompanying drawings (where like numbers represent likeelements), which form a part hereof, and in which is shown by way ofillustration specific example embodiments in which the variousembodiments may be practiced. These embodiments were described insufficient detail to enable those skilled in the art to practice theembodiments, but other embodiments may be used, and logical, mechanical,electrical, and other changes may be made without departing from thescope of the various embodiments. In the previous description, numerousspecific details were set forth to provide a thorough understanding ofthe various embodiments. But the various embodiments may be practicedwithout these specific details. In other instances, well-known circuits,structures, and techniques have not been shown in detail in order not toobscure embodiments.

Different instances of the word “embodiment” as used within thisspecification do not necessarily refer to the same embodiment, but theymay. Any data and data structures illustrated or described herein areexamples only, and in other embodiments, different amounts of data,types of data, fields, numbers and types of fields, field names, numbersand types of rows, records, entries, or organizations of data may beused. In addition, any data may be combined with logic, so that aseparate data structure may not be necessary. The previous detaileddescription is, therefore, not to be taken in a limiting sense.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Although the present disclosure has been described in terms of specificembodiments, it is anticipated that alterations and modification thereofwill become apparent to the skilled in the art. Therefore, it isintended that the following claims be interpreted as covering all suchalterations and modifications as fall within the true spirit and scopeof the disclosure.

What is claimed is:
 1. A method comprising: receiving a set of user dataassociated with a user currently reading electronic text on a device,the set of user data indicative of a reading retention of the user,wherein the set of data includes reading speed data based on a number ofwords read by the user over time, input interaction data based on userinputs from at least one input device coupled to the device indicativeof the user's interaction with the electronic text, and navigation dataindicative of the user's navigation patterns through the electronic texton the device; analyzing the set of user data to determine that the usercurrently reading electronic text on the device has a degraded retentionlevel based on a plurality of criteria, each criteria having a weight,wherein the plurality of criteria include a reading speed criterionbased on the reading speed data, an input interaction criterion based onthe input interaction data, and a navigation data criterion based on thenavigation data, wherein determining that the user has the degradedretention level indicates that a retention action should be issued; andexecuting, in response to determining that the user has the degradedretention level and thus the retention action should be issued, theretention action at the device the user is currently reading electronictext on to improve a retention level of the user beyond the degradedretention level in real-time.
 2. The method of claim 1, wherein theretention action includes changing a font of the electronic text.
 3. Themethod of claim 1, wherein analyzing the set of user data includesdetermining a biometric reading of the user, wherein a determination ismade that the retention action should be issued in response to thebiometric reading of the user being a predetermined amount below anaverage biometric reading of the user.
 4. The method of claim 1, whereina determination is made that the retention action should be issued basedon the retention level satisfying a retention level threshold.
 5. Themethod of claim 1, further comprising: storing, for the user, a set ofhistorical data indicating previous retention actions issued;determining, using the set of historical data, a previous retentionaction which was effective for the user; and selecting, in response todetermining the previous retention action which was effective, theprevious retention action for the user in response to a determinationthat a second retention action should be issued for the user.
 6. Themethod of claim 1, wherein the retention action includes scramblingletters of words within the electronic text.
 7. The method of claim 1,wherein the set of user data includes eye-tracking data, wherein thenumber of words read by the user over time is determined based on theeye-tracking data, wherein determining that the retention action shouldbe issued is completed based on the reading speed of the user fallingbelow a predetermined reading speed threshold.
 8. The method of claim 1,wherein the retention action includes closing an application running onthe device.
 9. The method of claim 1, further comprising: determiningwhether the retention action should be reverted based on collecting asecond set of user data; and reverting the retention action.